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Go Green, AI- For Earth’s Sake!

By Kapil Rajyaguru

Technological innovation and advancement have no doubt enhanced the life of humankind, but it goes without saying that our environment has always ended up paying a dear price in lieu of that. The world of web3 is today rocking with the buzzwords of artificial intelligence and its applications in modern day life, so let’s explore how generative Artificial Intelligence (AI) tools are affecting our environment, something that often gets overlooked in our zeal to become a technologically advanced race.

Environmental Impact of Artificial Intelligence

AI is quickly becoming new buzzword leading technological race of transformation. AI’s impact can be seen on various sectors like healthcare, entertainment and finance.

However, underneath the glowing benefits of AI lies an unmitigated consequence: An unavoidable carbon footprint.

Energy intensive generative AI tools consume vast amount of energy. The training of OpenAI’s GPT-4 requires large amount of computational power requiring substantial amount of electricity to energize the servers.

Apart from power consumption, maintenance of an ideal environment for these machines also requires cooling systems for servers, leading to vast amount of carbon emissions.

AI’s power consumption is huge compared to traditional digital activities.

You would be surprised to learn that in order to train a single large-scale AI model, it can emit as much carbon as five cars through their lifetimes, including that emitted during their manufacturing process.

Now a days, forecasting AI tools are becoming as common as search engines, causing more energy expenditure and in turn causing more damage to the environment.

The Energy Demands Of AI Models

The need for vast energy consumption by these generative AI models rises from following characteristics:

• Need for Vast Training Examples: In order to accurately identify an object, for example, a car, a model needs vast numbers of examples. These covers images of cars from various angles, colors and models. To train the model on identifying a car, it requires countless representations of cars so that the model can excel at identifying a new car.
• Requirement Of Multiple Training Cycles:  The training process itself is based on the trial and error method. If the model mistakes a car as a rocket then it adjusts its parameters, reclassifies the image appropriately and retrains itself.
• Retraining Requires New Information: The AI model requires fresh round of training when it comes across new category such as sports cars. The training model has to include various examples of sports cars, like yellow or blue ones, in the training set and begin new training cycle. 

Harnessing Green Energy

Scientists and researchers are coming up with way to utilize green (renewable) energy for the AI computation. If AI operations are energized using solar and wind power then it can significantly reduce the carbon footprint.

Tech giant Google is leading this revolution, pledging to power all its AI operations including data centers with renewable energy.

Much as the advantages of AI are great to ignore, the environmental impact is too vast to disregard. So, in order to harness the potential of AI we must pivot towards renewable energy to save our planet from its harmful effects.

The Role Of Policy And Regulations

The mechanism of checks and balances must be put in place just like other industries like finance and healthcare is governed by national and international policy and regulations. AI which could have vast impact on human beings must also be regulated with right policies and regulations in the larger interest of the society.

One of the key points behind regulating AI must be about curbing its negative environmental impact. New AI models must adapt to renewable energy so that AI can benefit human race in the long run without adversely affecting our planet.

After all, the goal of AI evolution can’t just be about making smarter machines; the goal must be to create smarter but responsible machine mechanism for a sustainable world where environmental and technological evolution can grow hand in hand, complimenting each other.

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